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Sentence comprehension
Sentence comprehension is an aspect of verbal comprehension and takes place whenever a reader or listener processes a language utterance, either in isolation or in the context of a conversation or a text. Many studies of the human language comprehension process have focused on reading of single utterances (sentences) without context. Extensive research has shown, however, that language comprehension is affected also by context preceding a given utterance, as well as many other factors. Ambiguity and sentence comprehension Sentence comprehension has to deal with ambiguity in spoken and written utterances, for example lexical, structural, and semantic ambiguities. Ambiguity is ubiquitous, but people usually resolve it so effortlessly that they don't even notice it. For example, the sentence Time flies like an arrow has (at least) the interpretations Time moves as quickly as an arrow, A special kind of fly, called time fly, likes arrows and Measure the speed of flies like you would measure the speed of an arrow. Usually, readers will be only aware of the first interpretation. Instances of ambiguity can be classified as local or global ambiguities. A sentence is globally ambiguous if it has two distinct interpretations. Examples are sentences like Someone shot the servant of the actress who was on the balcony. (was it the servant or the actress who was on the balcony?) or The cop chased the criminal with a fast car. (did the cop or the criminal have a fast car?). Comprehenders may have a preferential interpretation for either of these cases, but syntactically and semantically, neither of the possible interpretations can be ruled out. Local ambiguities persist only for a short amount of time as an utterance is heard or writtenand are resolved during the course of the utterance, so that the complete utterance has only one interpretation. Examples include sentences like The critic wrote the interview was enlightening, which is ambiguous when The critic wrote the book has been encountered, but was enlightening remains to be processed. At this point, the sentence could either end, stating that the critic is the author of the book, or it could go on to clarify that the critic wrote something about a book. The ambiguity ends at was enlightening, which determines that the second alternative is correct. When readers process a local ambiguity, they settle on one of the possible interpretations immediately, without waiting to hear or read more words that might help decide which interpretation is correct (this behaviour is called incremental processing). If they are surprised by the turn the sentence really takes, processing is slowed. This is visible for example in reading times. Locally ambiguous sentences therefore have been used as test cases to investigate the influence of a number of different factors on human sentence processing. If a factor helps readers to avoid difficulty, it is clear that this factor plays a factor in sentence processing. Theories about language comprehension Experimental research has spawned a large number of hypotheses about the architecture and mechanisms of sentence comprehension. Issues like modularity versus interactive processing and serial versus parallel computation of analyses have been theoretical divides in the field. Architectural issues Modular vs. interactive A modular view of sentence processing assumes that each factor involved in sentence processing is computed in its own module, which has limited means of communication with the other modules. For example, syntactic analysis creation takes place without input from semantic analysis or context-dependent information, which are processed separately. A common assumption of modular accounts is a feed-forward architecture, in which the output of one processing step is passed on to the next step without feedback mechanisms that would allow the output of the first module to be corrected. Syntactic processing is usually taken to be the most basic analysis step, which feeds into semantic processing and the inclusion of other information. Interactive accounts assume that all available information is processed at the same time and can immediately influence the computation of the final analysis. Serial vs. parallel Serial accounts assume that humans construct only one of the possible interpretations at first, and try another only if the first one turns out to be wrong. Parallel accounts assume the construction of multiple interpretations at the same time. To explain why comprehenders are usually only aware of one possible analysis of what they hear, models can assume that all analyses ranked, and the highest-ranking one is entertained. Models There are a number of influential models of human sentence processing that draw on different combinations of architectural choices. Garden path model The Garden Path Model is a serial modular parsing model. It proposes that a single parse is constructed by a syntactic module. Contextual and semantic factors influence processing at a later stage and can induce re-analysis of the syntactic parse. Re-analysis is costly and leads to an observable slowdown in reading. When the parser encounters an ambiguity, it is guided by two principles: Late Closure and Minimal Attachment. Late Closure causes new words or phrases to be attached to the current clause. For example, "John said he would leave yesterday" would be parsed as John said (he would leave yesterday), and not as John said (he would leave) yesterday (i.e., he spoke yesterday). Minimal Attachment is a strategy of parsimony: The parser builds the simplest syntactic structure possible (that is, the one with the fewest phrasal nodes). Constraint-based model Constraint-based theories of language comprehension emphasize how people make use of the vast amount of probabilistic information available in the linguistic signal. Through statistical learning, we pick up on the frequencies and distribution of events in our linguistic environments, which inform language comprehension. As such, language users are said to arrive at a particular interpretation over another during the comprehension of an ambiguous sentence by rapidly integrating these probabilistic constraints. Findings Lexical preferences lexical meaning influences sentence comprehension. “she saw her duck and some eggs” is a garden path sentence, makes one think of duck eggs; “She saw her duck and fall down” is different. (Boland 1997). second is slower response time (you have to build two VPs) Incomplete or inaccurate representations Christianson et al. (2001) asks, Do we construct structural representations of a sentence that are complete and accurate as we parse? While Anna dressed the baby played in the crib Did the baby play in the crib? (reader response 100%) Did Anna dress the baby? (errors) Ferreira (2003) People don't immediately read and comprehend a sentence. With active sentences, readers have 100% accuracy. With passive sentences (the man was bitten by the dog, the dog was bitten by the man) had 75% accuracy. Studies of incomplete representations emphasize the influence of expectations in sentence comprehension. Methods Behavioral tasks In behavioral studies, subjects are often presented with linguistic stimuli and asked to perform an action. For example, they may be asked to make a judgment about a word (lexical decision), reproduce the stimulus, or name a visually presented word aloud. Speed (often reaction time: time taken to respond to the stimulus) and accuracy (proportion of correct responses) are commonly employed measures of performance in behavioral tasks. Researchers infer that the nature of the underlying process(es) required by the task gives rise to differences; slower rates and lower accuracy on these tasks are taken as measures of increased difficulty. An important component of any behavioral task is that it stays relatively true to 'normal' language comprehension—the ability to generalize the results of any task is restricted when the task has little in common with how people actually encounter language. A common behavioral paradigm involves priming effects, wherein participants are presented first with a prime and then with a target word. The response time for the target word is affected by the relationship between the prime and the target. For example, Fischler (1977) investigated word encoding using the lexical decision task. She asked participants to make decisions about whether two strings of letters were English words. Sometimes the strings would be actual English words requiring a "yes" response, and other times they would be nonwords requiring a "no" response. A subset of the licit words were related semantically (e.g., cat-dog) while others were unrelated (e.g., bread-stem). Fischler found that related word pairs were responded to faster when compared to unrelated word pairs, which suggests that semantic relatedness can facilitate word encoding. Eye-movements Eye tracking has been used to study online language processing. This method has been influential in informing knowledge of reading. Additionally, Tanenhaus et al. (1995) established the visual world paradigm, which takes advantage of eye movements to study online spoken language processing. This area of research capitalizes on the linking hypothesis that eye movements are closely linked to the current focus of attention. Neuroimaging and Evoked Potentials The rise of non-invasive techniques provides myriad opportunities for examining the brain bases of language comprehension. Common examples include positron emission tomography (PET), functional magnetic resonance imaging (fMRI), event-related potentials (ERPs) in electroencephalography (EEG) and magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS). These techniques vary in their spatial and temporal resolutions (fMRI has a resolution of a few thousand neurons per pixel, and ERP has millisecond accuracy), and each type of methodology presents a set of advantages and disadvantages for studying a particular problem in language comprehension. Computational modeling Computational modeling is another means by which to explore language comprehension. Models, such as those instantiated in neural networks, are particularly useful because they requires theorists to be explicit in their hypotheses and because they can be used to generate accurate predictions for theoretical models that are so complex that they render discursive analysis unreliable. A classic example of computational modeling in language research is McClelland and Elman's TRACE model of speech perception.McClelland, J.L., & Elman, J.L. (1986). The TRACE model of speech perception. Cognitive Psychology, 18, 1-86 See also * Language Processing * Neurolinguistics * Prediction in Language Comprehension * Psycholinguistics * Reading Comprehension * Speech Perception References Further reading *Carroll, David, The Psychology of Language( Wadsworth Publishing, 2003)) * * External links * Human Sentence Processing: an introductory website on the computational psycholinguistic aspects of human sentence processing, developed for students in Linguistics, Pyschology or Computer Science. Category:Psycholinguistics Category:Verbal comprehension